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With the increasing use of assistive robots in rehabilitation and assisted mobility of human patients, there has been a need for a deeper understanding of human-robot interactions particularly through simulations, allowing an understanding of these interactions in a digital environment. There is an emphasis on accurately modelling personalised 3D human digital twins in these simulations, to glean more insights on human-robot interactions. In this paper, we propose to integrate personalised soft-body feet, generated using the motion capture data of real human subjects, into a skeletal model and train it with a walking control policy. Through evaluation using ground reaction force and joint angle results, the soft-body feet were able to generate ground reaction force results comparable to real measured data and closely follow joint angle results of the bare skeletal model and the reference motion. This presents an interesting avenue to produce a dynamically accurate human model in simulation driven by their own control policy while only seeing kinematic information during training., Comment: 10 pages, 16th International Conference on Social Robotics

This paper presents the syntax and reduction rules for an abstract machine based on the JavaScript XML language. We incorporate the notion of cost into our reduction rules, and create a type system that over-approximate this cost. This over-approximation results in an equation that may contain unknowns originating from while loops. We conclude with a formal proof of soundness of the type system for our abstract machine, demonstrating that it over-approximates the cost of any terminating program. An implementation of the type system, constraint gathering, and the abstract machine is also presented, Comment: Benjamin Bennetzen: 0009-0007-1751-6862 Daniel Vang Kleist: 0009-0005-1785-2124 Emilie Sonne Steinmann: 0009-0000-4733-5842 Loke Walsted: 0009-0002-1758-4594 Nikolaj Rossander Kristensen: 0009-0005-2339-8247 Peter Buus Steffensen: 0009-0005-6410-5869

Protecting confidential data from leaking is a critical challenge in computer systems, particularly given the growing number of observers on the internet. Therefore, limiting information flow using robust security policies becomes increasingly vital. We focus on the non-interference policy, where the goal is to ensure that confidential data can not impact public data. This paper presents a type system, for a subset of the ReScript syntax, designed to enforce non-interference. We conclude with a proof of soundness for the type system, demonstrating that if an expression is type-able, it is inherently non-interferent. In addition, we provide a brief overview of a type checker that implements the previously mentioned type system., Comment: Benjamin Bennetzen: 0009-0007-1751-6862 Daniel Vang Kleist: 0009-0005-1785-2124 Emilie Sonne Steinmann: 0009-0000-4733-5842 Loke Walsted: 0009-0002-1758-4594 Nikolaj Rossander Kristensen: 0009-0005-2339-8247 Peter Buus Steffensen: 0009-0005-6410-5869

Caring for the elderly, aging-in-place, and enabling the elderly to maintain a good life continue to be topics of increasing importance, especially in countries with a higher percentage of older people, as people live longer, and care-giving costs rise. This position paper proposes the concept of urban agetech, where agetech services beyond the home can be an integral part of a modern ageing-friendly city, and where support for the elderly, where needed, in the form of automated systems (e.g., robots and automated vehicles) would be a normal city function/service, akin to the rather commonplace public transport services today., Comment: 5 pages

In the noisy intermediate-scale quantum era, scientists are trying to improve the entanglement swapping success rate by researching anti-noise technology on the physical level, thereby obtaining a higher generation rate of long-distance entanglement. However, we may improve the generation rate from another perspective, which is studying an efficient entanglement swapping strategy. This paper analyzes the challenges faced by existing entanglement swapping strategies, including the node allocation principle, time synchronization, and processing of entanglement swapping failure. We present Parallel Segment Entanglement Swapping (PSES) to solve these problems. The core idea of PSES is to segment the path and perform parallel entanglement swapping between segments to improve the generation rate of long-distance entanglement. We construct a tree-like model as the carrier of PSES and propose heuristic algorithms called Layer Greedy and Segment Greedy to transform the path into a tree-like model. Moreover, we realize the time synchronization and design the on-demand retransmission mechanism to process entanglement swapping failure. The experiments show that PSES performs superiorly to other entanglement swapping strategies, and the on-demand retransmission mechanism can reduce the average entanglement swapping time by 80% and the average entanglement consumption by 80%., Comment: 9 pages, 8 figures. This paper has been accepted by IEEE International Conference on Quantum Communications, Networking, and Computing (QCNC 2024), Kanazawa, Japan, 2024, pp. 271-279, doi: 10.1109/QCNC62729.2024.00050

We show that Howe's big quotient is obtained via the tensoring over appropriate algebra.

Exposure to poor indoor air quality poses significant health risks, necessitating thorough assessment to mitigate associated dangers. This study aims to predict hourly indoor fine particulate matter (PM2.5) concentrations and investigate their correlation with outdoor PM2.5 levels across 24 distinct buildings in Australia. Indoor air quality data were gathered from 91 monitoring sensors in eight Australian cities spanning 2019 to 2022. Employing an innovative three-stage deep ensemble machine learning framework (DEML), comprising three base models (Support Vector Machine, Random Forest, and eXtreme Gradient Boosting) and two meta-models (Random Forest and Generalized Linear Model), hourly indoor PM2.5 concentrations were predicted. The model's accuracy was evaluated using a rolling windows approach, comparing its performance against three benchmark algorithms (SVM, RF, and XGBoost). Additionally, a correlation analysis assessed the relationship between indoor and outdoor PM2.5 concentrations. Results indicate that the DEML model consistently outperformed benchmark models, achieving an R2 ranging from 0.63 to 0.99 and RMSE from 0.01 to 0.663 mg/m3 for most sensors. Notably, outdoor PM2.5 concentrations significantly impacted indoor air quality, particularly evident during events like bushfires. This study underscores the importance of accurate indoor air quality prediction, crucial for developing location-specific early warning systems and informing effective interventions. By promoting protective behaviors, these efforts contribute to enhanced public health outcomes.

Deep learning algorithms lack human-interpretable accounts of how they transform raw visual input into a robust semantic understanding, which impedes comparisons between different architectures, training objectives, and the human brain. In this work, we take inspiration from neuroscience and employ representational approaches to shed light on how neural networks encode information at low (visual saliency) and high (semantic similarity) levels of abstraction. Moreover, we introduce a custom image dataset where we systematically manipulate salient and semantic information. We find that ResNets are more sensitive to saliency information than ViTs, when trained with object classification objectives. We uncover that networks suppress saliency in early layers, a process enhanced by natural language supervision (CLIP) in ResNets. CLIP also enhances semantic encoding in both architectures. Finally, we show that semantic encoding is a key factor in aligning AI with human visual perception, while saliency suppression is a non-brain-like strategy.

Obtaining reliable distance estimates to gas clouds within the Milky Way is challenging in the absence of certain tracers. The kinematic distance approach has been used as an alternative, derived from the assumption of circular trajectories around the Galactic centre. Consequently, significant errors are expected in regions where gas flow deviates from purely circular motions. We aim to quantify the systematic errors that arise from the kinematic distance method in the presence of a Galactic potential that is non-axisymmetric. We investigate how these errors differ in certain regions of the Galaxy and how they relate to the underlying dynamics. We perform 2D hydrodynamical simulation of the gas disk with the moving-mesh code Arepo, adding the capability of using an external potential provided by the Agama library for galactic dynamics. We introduce a new analytic potential of the Milky Way, taking elements from existing models and adjusting parameters to match recent observational constraints. In line with results of previous studies, we report significant errors in the kinematic distance estimate for gas close to the Sun, along sight lines towards the Galactic centre and anti-centre, and associated with the Galactic bar. Kinematic distance errors are low within the spiral arms as gas resides close to local potential minima and the resulting LOS velocity is similar to what is expected for an axisymmetric potential. Interarm regions exhibit large deviations at any given Galactic radius. This is caused by the gas being sped up or slowed down as it travels into or out of spiral arms. In addition, we identify 'zones of avoidance' in the lv-diagram, where the kinematic distance method is particularly unreliable and should only be used with caution, and we find a power law relation between the kinematic distance error and the deviation of the projected LOS velocity from circular motion., Comment: 23 pages, 20 figures, 6 tables, accepted for publication in A&A

The research of architecture has tremendous significance in realizing quantum Internet. Although there is not yet a standard quantum Internet architecture, the distributed architecture is one of the possible solutions, which utilizes quantum repeaters or dedicated entanglement sources in a flat structure for entanglement preparation & distribution. In this paper, we analyze the distributed architecture in detail and demonstrate that it has three limitations: 1) possible high maintenance overhead, 2) possible low-performance entanglement distribution, and 3) unable to support optimal entanglement routing. We design a hierarchical quantum Internet architecture and a communication model to solve the problems above. We also present a W-state Based Centralized Entanglement Preparation & Distribution (W-state Based CEPD) scheme and a Centralized Entanglement Routing (CER) algorithm within our hierarchical architecture and perform an experimental comparison with other entanglement preparation & distribution schemes and entanglement routing algorithms within the distributed architecture. The evaluation results show that the entanglement distribution efficiency of hierarchical architecture is 11.5% higher than that of distributed architecture on average (minimum 3.3%, maximum 37.3%), and the entanglement routing performance of hierarchical architecture is much better than that of a distributed architecture according to the fidelity and throughput., Comment: 17 pages, 17 figures. This paper has been published in IEEE Journal on Selected Areas in Communications

In this paper, we consider a risk process with deterministic growth and multiplicative jumps to model the capital of a low-income household. Reflecting the high-risk nature of the low-income environment, capital losses are assumed to be proportional to the level of accumulated capital at the jump time. Our aim is to derive the probability that a household falls below the poverty line, i.e. the trapping probability, where ``trapping" occurs when the level of capital of a household holds falls below the poverty line, to an area from which it is difficult to escape without external help. Considering the remaining proportion of capital to be distributed as a special case of the beta distribution, closed-form expressions for the trapping probability are obtained via analysis of the Laplace transform of the infinitesimal generator of the process. To study the impact of insurance on this probability, introduction of an insurance product offering proportional coverage is presented. The infinitesimal generator of the insured process gives rise to non-local differential equations. To overcome this, we propose a recursive method for deriving a closed-form solution of the integro-differential equation associated with the infinitesimal generator of the insured process and provide a numerical estimation method for obtaining the trapping probability. Constraints on the rate parameters of the process that prevent certain trapping are derived in both the uninsured and insured cases using classical results from risk theory.

We examine a stochastic formulation for data-driven optimization wherein the decision-maker is not privy to the true distribution, but has knowledge that it lies in some hypothesis set and possesses a historical data set, from which information about it can be gleaned. We define a prescriptive solution as a decision rule mapping such a data set to decisions. As there does not exist prescriptive solutions that are generalizable over the entire hypothesis set, we define out-of-sample optimality as a local average over a neighbourhood of hypotheses, and averaged over the sampling distribution. We prove sufficient conditions for local out-of-sample optimality, which reduces to functions of the sufficient statistic of the hypothesis family. We present an optimization problem that would solve for such an out-of-sample optimal solution, and does so efficiently by a combination of sampling and bisection search algorithms. Finally, we illustrate our model on the newsvendor model, and find strong performance when compared against alternatives in the literature. There are potential implications of our research on end-to-end learning and Bayesian optimization., Comment: 28 pages

This paper presents a wireless data collection framework that employs an unmanned aerial vehicle (UAV) to efficiently gather data from distributed IoT sensors deployed in a large area. Our approach takes into account the non-zero communication ranges of the sensors to optimize the flight path of the UAV, resulting in a variation of the Traveling Salesman Problem (TSP). We prove mathematically that the optimal waypoints for this TSP-variant problem are restricted to the boundaries of the sensor communication ranges, greatly reducing the solution space. Building on this finding, we develop a low-complexity UAV-assisted sensor data collection algorithm, and demonstrate its effectiveness in a selected use case where we minimize the total energy consumption of the UAV and sensors by jointly optimizing the UAV's travel distance and the sensors' communication ranges.